The fitting tag has no wiki summary.
1
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2answers
55 views
Difference between “estimated” and “fitted”?
Currently I am using a r package to fit an ARMA-GARCH process. Afterwards, I want to use the fitted values to calculate the Value at Risk. So these values are not the forecasts, but the ...
0
votes
1answer
33 views
Alternatives to least-Chi-square to fit a straight line,
as stated in the title I am looking for alternative ways to fit my data to a straight line. My current approach is a least Chi-Square Fitting, but the predicted relative errors for the slope exceed ...
0
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1answer
47 views
Robust distribution fitting?
I'm trying to fit a Johnson Unbounded distribution to a set of financial data with kurtosis and skewness, and also outliers. I started using Maximum Likelihood Estimators (MLE) but one outlier has too ...
2
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0answers
65 views
How to fit a standardized generalized hyperbolic distribution to my data using optim?
I want to fit a standardized generalized hyperbolid distribution to my data using the optim command.
Regarding my post here, I think I have to try it manually, by using the optim command.
My problem ...
1
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0answers
32 views
R Command to fit a standardized generalized hyperbolic distribution to data?
I wanted to ask, if there is a R command, which fits a standardized generalized hyperbolic distribution to my data? I know there is one, which fits a non-standardized, but I want to fit the ...
0
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0answers
21 views
weibull regression (without survival)
I have vectors $x$ and $y$, of the same size, where $x$ represents an intensity, and $y$ the corresponding probability of occurrence. I want to fit a Weibull distribution to this data. I would like ...
2
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1answer
74 views
Confusion with Chi-Square test
I have an extremely simple question regarding chi-square test. So far I have found two different formulas for it:
$$
\chi^2=\sum\frac{(x_o-x_e)^2}{x_e}
$$
and
$$
...
1
vote
1answer
45 views
Standardized generalized hyperbolic distribution
I am interested in the standardized version (mean zero, variance one) of the generalized hyperbolic and the hyperbolic distribution. I want to include this in my analysis and therefore I need the ...
1
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2answers
59 views
Line of best fit (Linear regression) over vertical line
I want to get a line of the best fit which is a line that passes as close as possible to a set of points defined by coordinates point_i = (X_i, Y_i).
When I apply linear regression, I have a special ...
2
votes
3answers
76 views
(Spline) curve fitting $(x,y)$ points when $dy/dx$ is also known for each point
I have an $(x,y)$ dataset consisting of points on an unknown curve and I've been using spline fits to generate a curve joining these points as a guide-to-the-eye (and nothing more). However, I also ...
1
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2answers
48 views
Kurtosis of a standardized Student's-t distribution?
I use the generalized form of the Student's-t distribution:
\begin{align*}
f(l|\nu ,\mu ,\beta) = \frac{\Gamma (\frac{\nu+1}{2})}{\Gamma (\frac{\nu}{2}) \sqrt{\pi \nu} \beta} ...
1
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1answer
46 views
Optim result highly dependent on starting value
I want to fit a standardized Student's-t distribution. The log-likelihood is given by:
\begin{align*}
log \mathcal{L}(\nu | l_1,...,l_n)=\sum_{i=1}^n \left( log \left( (\pi ...
11
votes
1answer
226 views
Detecting Outliers in Count Data
I have what I naively thought to be a fairly straight forward problem that involves outlier detection for many different sets of count data. Specifically, I want to determine if one or more values in ...
0
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0answers
59 views
Model vs observation, obtaining an optimal fit
This is a pretty general question regarding observations versus modelization and how to obtain the modeled set that best fits/reproduces an observed set of objects.
I have a given set made of ...
1
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1answer
59 views
Is this an error or not?
I am trying to fit a second order polynomial. I center and scale my predictors and fit the data using the lm function. I did ...
0
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1answer
105 views
Standard errors of hyperbFit?
I want to fit a hyperbolid distribution according to my notation:
\begin{align*}
H(l;\alpha,\beta,\mu,\delta)&=\frac{\sqrt{\alpha^2-\beta^2}}{2\alpha \delta K_1 (\delta\sqrt{\alpha^2-\beta^2})} ...
0
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0answers
51 views
Volatility model combined with different distributions?
I am working on fitting distributions to financial data using different volatility models. The simplest case of a gaussian distribution I do understand:
The data is $\mathcal{N}$$(\mu,\sigma^2)$ ...
2
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1answer
47 views
Combining standard errors of fit parameters
I have fitted a 3 parameter (mean, sigma & tau) model to my data and have also computed the standard error for each of them. The statistic of interest for my data is the sum of mean and tau. My ...
2
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1answer
128 views
Fit a smooth approximation line
I have a simulated data set and I want to fit a "smooth approximation line" like the image I have provided.
1- Is it possible to do this in Excel or Matlab? May I have a pointer on how to do it?
...
0
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1answer
48 views
$\chi^2$ parameter of a data fit
So what is that parameter. When I make a non-linear fit, the program gives me a value χ2/doF. What is it?
I know some statistics and I know those χ2 distributions are used for non-parametric ...
1
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1answer
81 views
Fitting t distribtution to financial data
The data I am using can be found here: http://uploadeasy.net/upload/nwv0.rar
The variable is called "alvsloss".
I want to fit a distribution to my financial data. First of all, I started with a ...
0
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0answers
64 views
Best fit of weighted mean?
The title might be somewhat misleading. I am trying to solve the following optimization problem and do not find any clues. Can any one help?
I have a sequence of $n$ observation $s_i$. Each ...
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0answers
15 views
Are there any fitting function like fitdistcens from fitdistplus but also works for binned data?
I have a set of binned data and I would like to fit to some distributions.
As I cannot find any package which is able to deal with binned data, I instead use ...
2
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0answers
33 views
Fitting series to a distribution
I am reading this note on Mean-CVAR optimization. The authors argue against the prevailing assumption that asset classes are normally distributed and propose using a truncated levy flight distribution ...
1
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0answers
111 views
Code for evaluating perplexity of an LDA topic model
I'm working on a research project where we are trying to assess model fit improvement of our LDA model (the basic Blie et al., 2003 LDA approach). I'm looking for code that allows us to assess the ...
1
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1answer
88 views
Interpreting non-visual linear regression results
I'm using the GSL statistical package to perform a linear regression on two sets of numbers. I don't have a heavy statistical background, and I'm used to seeing regressions presented in visual ...
2
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0answers
55 views
AIC/BIC: how many parameters does a permutation count for?
Let's say I have a model selection problem and I am trying to use AIC or BIC to evaluate the models. This is straightforward for models that have some number $k$ of real-valued parameters.
However, ...
1
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2answers
135 views
Multiple linear regression with restricted ranges in R
I'm trying to do a simple least-squares fit on some data. The formula is simply:
$Y = C_0X_1 + C_0X_2 + C_0X_3 + C_0X_4 + C_0X_5$
I have 24 rows of $Y$'s and $X$'s, and I'm trying to fit the $C$'s.
...
1
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0answers
71 views
Fitting data/determining parameter values for custom (biPareto) distribution
I have a dataset containing information about a bunch of wireless devices. Specifically, the number of other wireless devices each device encounters. Based on other researcher's prior work in the ...
1
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0answers
151 views
Fitting Lognormal Distribution in WinBugs
Let's suppose that some data points follow $Lognormal(\mu,\sigma^2)$ and both parameters are unknown . My goal is to obtain the posterior distribution by assigning conjugate prior distributions on ...
3
votes
1answer
91 views
How detailed should a data-driven Bayesian prior be?
My exact problem is this.
I have a number of sources of traffic with different conversion rates. I have good evidence that conversion rates vary based on the source. For each traffic source I have ...
0
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0answers
96 views
Model reduction - Backward elimination
The rule I use for reducing covariates (trimming) is as following:
Look at the highest order interaction. If that is not significant, drop it. If it is significant, stop.
If drop the highest order ...
1
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1answer
157 views
Efficient fitting of noncentral chi-squared distribution to data?
I am looking for the most efficient way to fit a noncentral chi-squared distribution with fixed d.o.f. to a given data set. So the inputs are d.o.f. and the data and the output should be the ...
1
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1answer
419 views
Fitting t-distribution in R: scaling parameter
How do I fit the parameters of a t-distribution, i.e. the parameters corresponding to the 'mean' and 'standard deviation' of a normal distribution. I assume they are called 'mean' and 'scaling/degrees ...
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0answers
228 views
MLE for Gamma Shifted Distribution
I need to fit a gamma distribution that is shifted to the left and truncated at zero (so that for example, my data may only come from the right tail of the full distribution, and I don't have any ...
4
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3answers
324 views
Can I use Kolmogorov-Smirnov test and estimate distribution parameters?
I've read that Kolmogorov-Smirnov test should not be used to test the goodness of fit of a distribution whose parameters have been estimated from the sample.
Does make sense to split my sample in two ...
1
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1answer
168 views
IID assumption for $Y_t=X_t-X_{t-1}$
I have some time series data, $X_t$, and I need to fit a heavy tail distribution to the first difference i.e. $Y_t=X_t-X_{t-1}$. Prior to fit this distribution, I need to test the iid (identical ...
1
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1answer
103 views
Difference between theoretical model and fit
I have two sets of experimental data for exponential decays of isotopes and I calculated the ratio of the two isotopes as a function of time. The theoretical model and the fit of the experimental data ...
1
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0answers
46 views
Does the EM algorithm require i.i.d?
The EM algorithm roughly has two steps.
E-Step:
Calculate the conditional expectation of the likelihood function given the data $x_1, . . . , x_n $ and the current estimates of parameters ...
1
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0answers
56 views
Fitting a distribution with ties in the data
I'd like to fit some standard distributions, say lognormal, to a set of data which unfortunately has ties. The precision of the measurement simply isn't high enough. Of course you can still fit the ...
2
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1answer
109 views
Compute gaussian fit corresponding to a discrete variable
I collected data for a discrete probability distribution and I have the pairs (value, probability).
...
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0answers
25 views
Gertensgabe and Werner plot
I was told about the Gertensgabe and Werner plot to assess the optimal threshold for a GPD model. However, I did not found neither any R package nor code to implement this. Does anybody have some ...
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0answers
70 views
Mixed tail distribution in R
I would like to fit a mixed-tail distribution in R. That is a distribution whose body pertains to a family (i.e. lognormal), the tail pertains to another family (i.e. GPD). The SAS proc severity ...
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0answers
46 views
Understanding ridge regresssion [duplicate]
Possible Duplicate:
How do you interpret the results from ridge regression?
I started learning ridge regression in R. I applied the linear ridge regression to my full data set and got the ...
1
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0answers
272 views
Fit Inverse Gamma Distribution to dataset in R
I am having some problems in modifying Procrastinator's code that assesses the fit of the Inverse Gamma distribution to some randomly generated data. I am refering to the code appearing in the ...
0
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1answer
76 views
Possibilities of variables combination and conclusion
I have 11 independent variables and one intercept. If I am right, that means there are $2^{12}=4096$ possible different models, right?
So does this mean, if I calculate each model and save the ...
4
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2answers
266 views
How to bootstrap the best fit distribution to a sample?
If I have a sample:
set.seed(0)
x <- rlnorm(500)
Then I can use the fit.distr function to find the best fit among two candidate distributions, e.g.
...
1
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3answers
140 views
Generating random nos based on 'k' moments
How do I generate random nos based on say k moments? (no other constraints on support)
When k = 2, we generate random nos. from a normal distribution defined by the 2 moments. Can we generalize this ...
2
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0answers
164 views
Dealing with poor fit in an Item Response Theory model
I'm studying an online course with about 3000 students who each took several quizzes and I'm trying to apply Item Response Theory (using the ltm package in R) to model the questions, determine which ...
2
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2answers
345 views
“Least square root” fitting? A fitting method with multiple minima
I am looking for the name of a fitting method that will work even if points from multiple dataseries are meshed together.
As far as I understand there are two major methods, least squares and least ...
